Presence Of Multiple Sources Research Articles

Subsurface Profiling Using Roadside MASW Survey: Influence of Multiple Sources and Offline Distance

Roadside MASW survey utilizes traffic-generated surface wave signals for subsurface characterization and, thus, can be a useful geophysical method, especially in urban areas. However, such signals originating from vehicular movements over road surface irregularities, or sources, produce complex field records of multi-source and multi-azimuthal characteristics. Such sources are termed intra-line if they exist within the receiver spread and outer-line when they exist outside the receiver spread. In a roadside survey, the receiver spread is placed outside and parallel to the centreline of the road, thereby creating an offline distance with respect to the sources on the road. In this study, experimental investigations are conducted to determine the influence of the presence of intra-line or outer-line sources and offline distances of source positioning on the dispersion imaging of roadside MASW records. Artificial hurdles were placed deliberately at different positions on an adjacent road to determine the influence of intra-line and outer-line sources. Furthermore, receiver arrays were placed at varying offline distances with respect to the centreline of the adjacent road to assess the effect of offline distance on the dispersion imaging and subsequent shear wave velocity profile. The study finds that the dispersion images obtained from the intra-line source have better resolutions compared to those obtained from the outer-line source. Further, the presence of multiple sources during the data acquisition does not necessarily shed any detrimental influence on dispersion imaging as long as there is no contamination and mutual interferences of the raw wavefield records. As the offline distance increases, the intensity of the traffic-generated source signal diminishes. It is observed typically for the studied site that beyond an offline distance of 15 m, there remains no recognizable energy to obtain a distinct dispersion image. A comparative study of the shear wave velocity profiles obtained from a borehole, roadside, active, and passive remote MASW surveys revealed an agreeable match, thereby indicating the usability of the roadside MASW survey, especially when offline distance is not enormously large.

A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.

Evaluation of hierarchical controls to manage power, energy and daily operation of remote off-grid power systems

Integration of locally available renewable energy sources and energy storage have proven to be a cost-effective and eco-friendly retrofit for the primarily diesel-based remote off-grid power systems. However, in the presence of multiple sources and energy storage, the energy management and power management mechanisms must be properly designed to enhance their envisioned benefits while assuring stability, availability, and security of power supply. This paper presents a hierarchical controller equipped with both energy management and power management strategies to obtain an optimum and stable system operation subjecting remote off-grid power systems. An operation evaluation framework is deployed to assess the applicability of the derived optimum operational routines as well as to verify the competence of the low-level controls. Results are demonstrated for a remote off-grid PV-Diesel-Battery power system under different diurnal and seasonal variations. Results revealed the competence of incorporated power management strategies as well as the use of proposed operation evaluation mechanism in identifying certain risks associated with optimum operational routines derived through energy management systems. The overall study confirmed that a coordinated control and operation evaluation platform is required to achieve a logical and quantitatively optimized operational trajectory.

Robust Wideband DOA Estimation Based on Element-Space Data Reconstruction in a Multi-Source Environment

In the presence of multiple sources, the performance of direction-of-arrival (DOA) estimation based on beam power maximization is susceptible to the energy leaking from the interference beams to the target beam, especially in the case that the signal of interest (SOI) is quite weak. To address this issue, a robust wideband DOA estimation method is proposed in this paper. Unlike those conventional high-resolution methods which mitigate the influence of energy leakage by reducing beamwidths and sidelobe levels, the proposed method achieves this by directly reconstructing the element-space data to approach the received hydrophone data of the single-source scenario containing only the SOI. Element-space data reconstruction (ESDR) for the SOI is achieved by removing the element-space waveforms of all the interference signals from the received hydrophone data. Moreover, an iterative algorithm is developed to adaptively extract the element-space waveform of each interference signal, without requiring the prior information of the array amplitude response coefficients and hydrophone coordinates of a distorted towed array. Simulation results show that the proposed method outperforms its counterparts in terms of estimation accuracy for a multi-source scenario. Meanwhile, the DOA estimation performance of the proposed method in a multi-source environment is close to that obtained by the beam power maximization method in the single-source scenario, even if the signal-to-interference ratio (SIR) is as low as −25 dB. At-sea experimental results prove that, even though the number of signal sources is as many as seven and the SOI is contaminated by real ocean ambient noise, the proposed method still achieves a better DOA estimation performance compared to existing state-of-the-art methods.

Atmospheric deposition of organic matter at a remote site in the central Mediterranean Sea: implications for the marine ecosystem

Abstract. Atmospheric fluxes of dissolved organic matter (DOM) were studied for the first time on the island of Lampedusa, a remote site in the central Mediterranean Sea (Med Sea), between 19 March 2015 and 1 April 2017. The main goals of this study were to quantify total atmospheric deposition of DOM in this area and to evaluate the impact of Saharan dust deposition on DOM dynamics in the surface waters of the Mediterranean Sea. Our data show high variability in DOM deposition rates without a clear seasonality and a dissolved organic carbon (DOC) input from the atmosphere of 120.7 mmol DOC m−2 yr−1. Over the entire time series, the average dissolved organic phosphorus (DOP) and dissolved organic nitrogen (DON) contributions to the total dissolved pools were 40 % and 26 %, respectively. The data on atmospheric elemental ratios also show that each deposition event is characterized by a specific elemental ratio, suggesting a high variability in DOM composition and the presence of multiple sources. This study indicates that the organic substances transported by Saharan dust on Lampedusa mainly come from a natural sea spray and that Saharan dust can be an important carrier of organic substances even though the load of DOC associated with dust is highly variable. Our estimates suggest that atmospheric input has a larger impact on the Med Sea than on the global ocean. Further, DOC fluxes from the atmosphere to the Med Sea can be up to 6 times larger than total river input. Longer time series combined with modeling would greatly improve our understanding of the response of DOM dynamics in the Med Sea to the change in aerosol deposition pattern due to the effect of climate change.

Dissolved Organic Matter in the Gulf of Cádiz: Distribution and Drivers of Chromophoric and Fluorescent Properties

The Gulf of Cadiz (GoC) connects the Mediterranean Sea with the Atlantic Ocean through the Strait of Gibraltar. Particular hydrographic processes take place in the GoC, such as riverine discharges and surface circulation marked by wind-induced seasonal upwelling. Although physical processes have been widely studied, little is known about the biogeochemical processes that occur in the basin, especially those involving organic matter. Therefore, vertical and seasonal dynamics of dissolved organic carbon (DOC) and dissolved organic matter (DOM) optical properties (absorption and fluorescence) were measured in 766 samples collected between 5 and 800 m depth during 4 oceanographic cruises to obtain quantitative and qualitative information about DOM in the GoC. We performed parallel factor analysis (PARAFAC) to identify the main fluorophores present in the GoC, and an optimum multiparameter water mass analysis to differentiate the effect of water mass mixing from the biogeochemical processes in deep waters. PARAFAC analysis validated six fluorescent components; three humic-like, two protein-like, and a possible mixture of polycyclic aromatic hydrocarbon-like with protein-like material. DOC average concentration was 77.0 ± 12.7 µM, with higher values in surface and coastal waters during summer, mainly related to primary production. Linear relationships between DOC and apparent oxygen utilization indicate differences in oxygen consumption within the deep waters, which could be related to upwelling zones. Seasonal and spatial differences were also observed in fluorescent DOM distribution. Protein-like components were the most abundant fraction, with an average contribution of 64.75 % ± 7.85 %, being higher in summer and surface waters, associated with an increase in biological activity. Our results indicate that water mass mixing is the main driver of the major humic-like components, while biogeochemical processes at a local scale explain DOC and protein-like components distribution. Our findings suggest that modeling DOM dynamics in the GoC is complicated due to its complex hydrography and the presence of multiple sources and sinks of DOM.

Time-dependent radiation hydrodynamics on a moving mesh

ABSTRACT We describe the structure and implementation of a radiation hydrodynamic solver for manga, the moving-mesh hydrodynamics module of the large-scale parallel code, Charm N-body GrAvity solver (changa). We solve the equations of time-dependent radiative transfer (RT) using a reduced speed of light approximation following the algorithm of Jiang et al. By writing the RT equations as a generalized conservation equation, we solve the transport part of these equations on an unstructured Voronoi mesh. We then solve the source part of the RT equations following Jiang et al. using an implicit solver, and couple this to the hydrodynamic equations. The use of an implicit solver ensures reliable convergence and preserves the conservation properties of these equations even in situations where the source terms are stiff due to the small coupling time-scales between radiation and matter. We present the results of a limited number of test cases (energy conservation, momentum conservation, dynamic diffusion, linear waves, crossing beams, and multiple shadows) to show convergence with analytic results and numerical stability. We also show that it produces qualitatively the correct results in the presence of multiple sources in the optically thin case.

Identification of Smart Structures with Robust Control under Stochastic Excitation

In light of past research in this field, this paper intends to discuss some innovative approaches in vibration control of smart structures, particularly in the case of structures with embedded piezoelectric materials. In this work, we review the principal problems in mechanical engineering that the structural control engineer has to address when designing robust control laws: structural modeling techniques, uncertainty modeling, and robustness validation under stochastic excitation. Control laws are desirable for systems where guaranteed stability or performance is required despite the presence of multiple sources of uncertainty.

Identification of Smart Structures with Robust Control under Stochastic Excitation

In light of past research in this field, this paper intends to discuss some innovative approaches in vibration control of smart structures, particularly in the case of structures with embedded piezoelectric materials. In this work, we review the principal problems in mechanical engineering that the structural control engineer has to address when designing robust control laws: structural modeling techniques, uncertainty modeling, and robustness validation under stochastic excitation. Control laws are desirable for systems where guaranteed stability or performance is required despite the presence of multiple sources of uncertainty.

Coherency structure exploitation in DOA estimation for non‐circular sources

This study addresses the problem of direction-of-arrival (DOA) estimation for non-circular (NC) sources in the presence of multipath propagation. Recent research reveals that the use of the association of incident signals, i.e. source association (SA), can provide a significant gain in reducing DOA estimation error, especially in the presence of multiple sources. Here, in order to make full use of both the properties of NC sources and the SA information, the multipath model with NC sources is studied, and then a simple combination algorithm is proposed to estimate the SA, based on which, the authors derive an iterative root estimator to refine a given coarse DOA estimation. Meanwhile, the stochastic Cramer-Rao bound taking these two factors into account is derived as well. The proposed method is search-free and maintains the array aperture which is up to twice of the physical array aperture. Numerical simulations have been provided to verify the effectiveness of the proposed method.

Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

Azimuth angle estimation using a single vector hydrophone is a well-known problem in underwater acoustics. In the presence of multiple sources, a conventional complex acoustic intensity estimator (CAIE) cannot distinguish the azimuth angle of each source. In this paper, we propose a steering acoustic intensity estimator (SAIE) for azimuth angle estimation in the presence of interference. The azimuth angle of the interference is known in advance from the global positioning system (GPS) and compass data. By constructing the steering acoustic energy fluxes in the x and y channels of the acoustic vector hydrophone, the azimuth angle of interest can be obtained when the steering azimuth angle is directed toward the interference. Simulation results show that the SAIE outperforms the CAIE and is insensitive to the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR). A sea trial is presented that verifies the validity of the proposed method.

Multiphysics fluid monitoring: Toward targeted monitoring design under uncertainty

Properly designed multiphysics measurements program can improve the accuracy of fluid front monitoring (FFM) by combining tools with various spatial resolutions and desired contrast in time-lapse measurements, consequently enabling better sweep efficiency and increased oil recovery. We have introduced a new workflow for multiphysics FFM feasibility studies that determines the suitability of measurements considered for monitoring and enables informed decision making on where, when, and how often the measurements need to be performed. The workflow integrates petrophysically and thermodynamically consistent multiphysics responses for seismic, electromagnetic, and neutron capture measurements. We argue that, in the presence of multiple sources of uncertainty, reservoir performance should be analyzed from a 4D probabilistic standpoint, rather than just by looking at a traditional spread in cumulative production curves. Consequently, the monitoring program should be designed around our understanding of reservoir 4D probabilistic performance through consistent multiphysics modeling. We have developed a set of approaches to enable addressing both tasks on a single platform with all relevant sources of uncertainties including parametric and model uncertainties in effective medium modeling and reservoir simulation. The developed workflow is illustrated using the ISAPP Field Development Optimization Challenge benchmark data set introduced in 2017.

Aircraft integrated structural health monitoring using lasers, piezoelectricity, and fiber optics

Various structural health monitoring (SHM) systems that have been developed based on laser ultrasonics and fiber optics are introduced in this paper. The systems are used to realize the new SHM paradigm for ground SHM, called the Smart Hangar. Guided wave ultrasonic propagation imaging (G-UPI) technology is implemented in the Smart Hangar in the form of built-in and mobile G-UPI systems. The laser-induced guided waves generated by pulsed beam scanning in the wings and fuselage of an aircraft are captured by a fiber optic, piezoelectric, or laser ultrasonic sensor, and their propagation is visualized. The wave propagation video is further processed to visualize damage in the presence of multiple sources using a multi-time-frame ultrasonic energy mapping (mUEM) method. For in-flight SHM, laser scanning-based smartification of the structure with sensors is presented, and an event localization method based on fiber optics and piezoelectric sensing is also introduced. Optic sensors are also utilized to reconstruct the wing deformation from the measured strain. The wing deformation and impact localization information is transferred to a ground pilot in the case of unmanned aerial vehicles (UAV), and the ground pilot can feel the wing deformation and impact by using a pilot arm-wearable haptic interface, which makes it possible to achieve human-UAV interactive decision making.

Statistical signatures of a targeted search by bacteria

Chemoattractant gradients are rarely well-controlled in nature and recent attention has turned to bacterial chemotaxis toward typical bacterial food sources such as food patches or even bacterial prey. In environments with localized food sources reminiscent of a bacterium’s natural habitat, striking phenomena—such as the volcano effect or banding—have been predicted or expected to emerge from chemotactic models. However, in practice, from limited bacterial trajectory data it is difficult to distinguish targeted searches from an untargeted search strategy for food sources. Here we use a theoretical model to identify statistical signatures of a targeted search toward point food sources, such as prey. Our model is constructed on the basis that bacteria use temporal comparisons to bias their random walk, exhibit finite memory and are subject to random (Brownian) motion as well as signaling noise. The advantage with using a stochastic model-based approach is that a stochastic model may be parametrized from individual stochastic bacterial trajectories but may then be used to generate a very large number of simulated trajectories to explore average behaviors obtained from stochastic search strategies. For example, our model predicts that a bacterium’s diffusion coefficient increases as it approaches the point source and that, in the presence of multiple sources, bacteria may take substantially longer to locate their first source giving the impression of an untargeted search strategy.

Subarray processing with coprime and minimum redundancy arrays

A coprime sensor array (CSA) refers to a non-uniform linear array consisting of two undersampled uniform linear arrays (ULAs) with coprime numbers of elements, respectively. Its beam pattern can be enhanced by multiplying beampatterns of individual subarrays. This technique, called product processing, provides improved resolution because its sum coarray length is extended from the product of beampatterns. Nevertheless, the extended coarray inevitably includes holes that result in higher peak sidelobes than the full ULA case. In this paper, we propose a technique to reduce peak sidelobes through the combination with minimum redundancy arrays (MRAs) and an additional subarray. Two ULAs comprising the coprime array are thinned by the MRA technique, and the reduced number of sensors is then utilized to construct the third subarray. The third array suppresses peak sidelobes by placing nulls at the peak sidelobe locations. The characteristics of the proposed technique are analyzed in terms of white noise gain (WNG) and maximum sidelobe level (MSL). It is demonstrated that the proposed technique can improve the MSL with similar number of sensors, thereby reducing the estimation error in the presence of multiple sources.

Tackling the salinity-pollution nexus in coastal aquifers from arid regions using nitrate and boron isotopes.

Salinization and nitrate pollution are generally ascertained as the main issues affecting coastal aquifers worldwide. In arid zones, where agricultural activities also result in soil salinization, both phenomena tend to co-exist and synergically contribute to alter groundwater quality, with severe negative impacts on human populations and natural ecosystems' wellbeing. It becomes therefore necessary to understand if and to what extent integrated hydrogeochemical tools can help in distinguishing among possible different salinization and nitrate contamination origins, in order to provide adequate science-based support to local development and environmental protection. The alluvial plain of Bou-Areg (North Morocco) extends over about 190km2 and is separated from the Mediterranean Sea by the coastal Lagoon of Nador. Its surface is covered for more than 60% by agricultural activities, although the region has been recently concerned by urban population increase and tourism expansion. All these activities mainly rely on groundwater exploitation and at the same time are the main causes of both aquifer and lagoon water quality degradation. For this reason, it was chosen as a case study representative of the typical situation of coastal aquifers in arid zones worldwide, where a clear identification of salinization and pollution sources is fundamental for the implementation of locally oriented remedies and long-term management strategies. Results of a hydrogeochemical investigation performed between 2009 and 2011 show that the Bou-Areg aquifer presents high salinity (often exceeding 100mg/L in TDS) due to both natural and anthropogenic processes. The area is also impacted by nitrate contamination, with concentrations generally exceeding the WHO statutory limits for drinking water (50mg/L) and reaching up to about 300mg/L, in both the rural and urban/peri-urban areas. The isotopic composition of dissolved nitrates (δ15NNO3 and δ18ONO) was used to constrain pollution drivers. The results indicate two main origins for human-induced pollution: (i) manure and septic effluents, especially in urban areas, and (ii) synthetic fertilizers in agricultural areas. In the latter, δ15N-enriched values highlight a mixture of those sources, possibly related to unbalanced fertilization and agricultural return flow. Boron isotopes (δ11B) were hence studied to further distinguish the nitrate origin in the presence of multiple sources and mixing processes. The results indicate that in the study area, the high geochemical background for B and Cl, associated to the complex water-rock interaction processes, limit the application of the coupled δ11B and δ15N isotopic systematics to the detection of sources of groundwater pollution. In fact, despite the exceedingly high nitrate contents, the depleted δ11B values that characterize synthetic fertilizers and sewage leakages could not be detected. Therefore, even if in saline groundwater the anthropogenic contribution has a negligible effect in terms of salinity input, with both sewage and irrigation water not very charged, the associated nitrate content fuels up water-rock interaction processes, eventually leading to a mineralization increase.

A multichannel diffuse power estimator for dereverberation in the presence of multiple sources

Using a recently proposed informed spatial filter, it is possible to effectively and robustly reduce reverberation from speech signals captured in noisy environments using multiple microphones. Late reverberation can be modeled by a diffuse sound field with a time-varying power spectral density (PSD). To attain reverberation reduction using this spatial filter, an accurate estimate of the diffuse sound PSD is required. In this work, a method is proposed to estimate the diffuse sound PSD from a set of reference signals by blocking the direct signal components. By considering multiple plane waves in the signal model to describe the direct sound, the method is suitable in the presence of multiple simultaneously active speakers. The proposed diffuse sound PSD estimator is analyzed and compared to existing estimators. In addition, the performance of the spatial filter computed with the diffuse sound PSD estimate is analyzed using simulated and measured room impulse responses in noisy environments with stationary noise and non-stationary babble noise.

Dendrochronological Studies of Alder (Alnus Glutinosa) on Scottish Crannogs

On most Scottish crannogs that have been investigated alder is the species most extensively used for construction and therefore has the potential to provide fine chronological resolution for these sites. Dendrochronological studies of alder have now been undertaken on three crannogs, with mixed results. At Buiston the construction of a single comprehensive alder chronology has contributed significantly to the overall chronology of the crannog, whereas at Oakbank and Cults Loch 3 it was only possible to construct numerous small chronologies which have limited value for chronological resolution on the sites. Comparison between the datasets suggest that factors such as the structure of the parent tree, i.e. whether it comes from multi-stemmed coppice or single maiden trees, and the presence of multiple sources are likely to be significant factors in the successful dendro-dating of the species.

Signal processing for a positioning system with binary sensory outputs

Energy based localization method for source localization has attracted considerable attention in recent years. The binary sensor is a low-power and bandwidth-efficient solution for positioning system since the node provides only binary information about the sources. Most of the existing acoustic source localization methods assume there only exists a single source and may fail with the presence of multiple sources. In contrast, we directly consider the case when the node is influenced by multiple sources. A maximum likelihood estimation method is applicable to the multiple sources localization problems, but is at the cost of high computational complexity. To tackle these problems, this paper proposes a novel likelihood matrix based multiple acoustic sources localization algorithm for binary sensor. The fuzzy c-means algorithm is firstly employed to calculate the membership degrees of the alarmed nodes associated with the sources, and then a likelihood matrix is proposed for multiple acoustic sources localization. Simulation and experiment results show that the proposed algorithm provides accurate location estimations.

A hearing-inspired approach for distant-microphone speech recognition in the presence of multiple sources

This paper addresses the problem of speech recognition in reverberant multisource noise conditions using distant binaural microphones. Our scheme employs a two-stage fragment decoding approach inspired by Bregman's account of auditory scene analysis, in which innate primitive grouping ‘rules’ are balanced by the role of learnt schema-driven processes. First, the acoustic mixture is split into local time-frequency fragments of individual sound sources using signal-level primitive grouping cues. Second, statistical models are employed to select fragments belonging to the sound source of interest, and the hypothesis-driven stage simultaneously searches for the most probable speech/background segmentation and the corresponding acoustic model state sequence. The paper reports recent advances in combining adaptive noise floor modelling and binaural localisation cues within this framework. By integrating signal-level grouping cues with acoustic models of the target sound source in a probabilistic framework, the system is able to simultaneously separate and recognise the sound of interest from the mixture, and derive significant recognition performance benefits from different grouping cue estimates despite their inherent unreliability in noisy conditions. Finally, the paper will show that missing data imputation can be applied via fragment decoding to allow reconstruction of a clean spectrogram that can be further processed and used as input to conventional ASR systems. The best performing system achieves an average keyword recognition accuracy of 85.83% on the PASCAL CHiME Challenge task.